[1] [1] Pneumonia in children[EB/OL].(2022-11-11) [2023-02-18]. https://www.who.int/news-room/fact-sheets/detail/pneumonia.

[3] [3] ZHENG S, GUO J, CUI X, et al. Automatic pulmonary nodule detection in CT scans using convolutional neural networks based on maximum intensity projection[J]. IEEE Transactions on Medical Imaging, 2020, 39(3): 797-805.

[5] [5] WANG X, PENG Y, LU L, et al. ChestX-Ray8: hospital-scale chest X-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2017:3462-3471.

[6] [6] IRVIN J, RAJPURKAR P, KO M, et al. CheXpert: a large chest radiograph dataset with uncertainty labels and expert comparison[C]//AAAI Conference on Artificial Intelligence. Honolulu, Hawaii, USA. Washington: AAAI, 2019, 33(01): 590-597.

[7] [7] WANG H, WANG S, QIN Z, et al. Triple attention learning for classification of 14 thoracic diseases using chest radiography[J]. Medical Image Analysis, 2021, 67:101846.

[8] [8] LEE Y W, HUANG S K, CHANG R F. CheXGAT: a disease correlation-aware network for thorax disease diagnosis from chest X-ray images[J]. Artificial Intelligence in Medicine, 2022, 132:102382.

[9] [9] ZHU X, PANG S, ZHANG X, et al. PCAN: pixel-wise classification and attention network for thoracic disease classification and weakly supervised localization[J]. Computerized Medical Imaging and Graphics, 2022, 102:102137.

[10] [10] HO T K K, GWAK J. Utilizing knowledge distillation in deep learning for classification of chest X-ray abnormalities[J]. IEEE Access, 2020, 8:160749-160761.

[11] [11] ZHAO M, ZHONG S, FU X, et al. Deep residual shrinkage networks for fault diagnosis[J]. IEEE Transactions on Industrial Informatics, 2020, 16(7): 4681-4690.

[12] [12] LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(2): 318-327.

[13] [13] MEHTA S, RASTEGARI M. MobileViT: light-weight, general-purpose, and mobile-friendly vision transformer[C]//International Conference on Learning Representations, April 25-29, 2022, New Orleans. Washington: ICLR, 2022.

[14] [14] ZAGORUYKO S, KOMODAKIS N. Paying more attention to attention: improving the performance of convolutional neural networks via attention transfer[C]//2017 International Conference on Learning Representations, April 24-26, 2017, Toulon, France. Washington: ICLR, 2017.

[15] [15] TUNG F, MORI G. Similarity-preserving knowledge distillation[C]//2019 IEEE/CVF International Conference on Computer Vision (ICCV), October 27-November 2, 2019, Seoul, Korea (South). New York: IEEE, 2019:1365-1374.

[16] [16] CAO Y, XU J, LIN S, et al. GCNet: non-local networks meet squeeze-excitation networks and beyond[C]//2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), October 27-28, Seoul, Korea (South). New York: IEEE, 2019:1971-1980.

[17] [17] SELVARAJU R R, COGSWELL M, DAS A, et al. Grad-CAM: visual explanations from deep networks via gradient-based localization[C]//2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy. New York: IEEE.2017:618-626.